Abstract: The rapidly developing field of "Commercial Operation Divergence Analysis," this research seeks to identify and understand differences in commercial systems that exceed expected results. Approaches in this domain aim to identify the characteristics of process implementations that are associated with changes in process effectiveness. This entails identifying the features of procedural behaviours that result in unpleasant results and figuring out which behaviours have the biggest impact on increased efficiency. As the scale and complexity of big data management and process mining continue to expand, the threat of cyber-attacks poses a critical challenge. This research leverages machine learning techniques for the detection and defence against cyber threats within the realm of big data management and process mining. The study introduces novel metrics such as Skewness, Coefficient of Variation, Standard Deviation, Maximum, Minimum, and Mean for assessing the security state, utilizing variables like SPI, SPEI, and SSI. The research addresses prior issues in cyber-attack detection by integrating machine learning into the specific context of big data and process mining. The novelty lies in the application of Skewness and other statistical metrics to enhance the precision of threat detection. The results demonstrate the effectiveness of the proposed methodology, showcasing promising outcomes in identifying and mitigating cyber threats in the given dataset and which makes use of Support Vector Regression (SVR), has a standard deviation of 0.9, which is consistent with the variability shown in SVM. The results demonstrate a significant achievement, with a Mean Absolute Error (MAE) of 0.98, indicating the efficacy of the proposed approach in providing accurate and timely insights for cyberattack detection and defense, thereby enhancing the overall security posture in data-intensive systems. The results highlight how well the proposed method extracts significant insights from complicated event data, with important ramifications for real-world application and decision-making procedures.

Taviti Naidu Gongada, Amit Agnihotri, Kathari Santosh, Vijayalakshmi Ponnuswamy, Narendran S, Tripti Sharma and Yousef A.Baker El-Ebiary. “Leveraging Machine Learning for Enhanced Cyber Attack Detection and Defence in Big Data Management and Process Mining”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.2 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150266

@article{Gongada2024,
title = {Leveraging Machine Learning for Enhanced Cyber Attack Detection and Defence in Big Data Management and Process Mining},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150266},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150266},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {2},
author = {Taviti Naidu Gongada and Amit Agnihotri and Kathari Santosh and Vijayalakshmi Ponnuswamy and Narendran S and Tripti Sharma and Yousef A.Baker El-Ebiary}
}